Automated sentiment analysis systems enable computerized systems to process large volumes of human-generated information, such as online product reviews and social media posts, to understand consumer needs, sketch marketing strategies, and identify potential areas of improvement. This is especially true for aspect-level sentiment analysis, which detects user opinions on each aspect of a product or service. Compared to review level sentiment analysis, aspect-level system can provide more detailed information for market analysis. For example: a review-level sentiment analysis system can summarize from online user reviews that users like a particular drill driver, but an aspect-level system will report that users express a positive sentiment toward the drill driver because it is powerful, but further suggest that the sentiment can be improved if the drill driver were to have a longer battery life.
In practice, aspect-level sentiment analysis systems usually first detect aspects in user reviews with a predefined ontology which contains the common aspects of a product (e.g., power, price, battery, etc.), and then try to predict the reviewers' sentiment polarity towards these aspects. The term “sentiment polarity” indicates that reviewers have a “positive” sentiment for a particular aspect of the product that indicates a preference in favor of the product while a negative polarity indicates a preference against the aspect of the product. In aspect sentiment analysis, a single reviewer may express sentiments with a positive polarity towards some aspects of a product while expressing sentiments with a negative polarity towards other aspects of the product.
While some forms of aspect sentiment analysis are known to the art, the present systems require a great deal of human effort to classify or “annotate” a large body of reviews for products in a particular domain to enable a machine-learning process to produce a sentiment analysis model that is then used in an automated aspect-level sentiment analysis system. In the prior art, the manual annotation process must be repeated for specific sets of training data that apply to a specific domain. For example, a training data set for one domain includes manually annotated data that contain sentiments of users towards restaurants, which enables a machine learning process to generate a sentiment analysis model to evaluate the positive or negative sentiments towards specific aspects of additional restaurant reviews. However, to generate another sentiment analysis model in a different domain, such as consumer electronics, the annotated training data pertaining to the restaurant domain do not provide relevant information that produces a useful aspect-level model to identify the sentiments towards consumer electronics. Instead, the same manual annotation process must be applied to a large set of reviews that are relevant to the domain of consumer electronics to provide training data to generate a useful sentiment analysis model for the consumer electronics domain. Given these drawbacks of the prior art, improvements to sentiment analysis systems and methods that reduce the requirements for human annotation to generate trained sentiment analysis models would be beneficial.